FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity. - Martin Sícho; Christina de Bruyn Kops; Conrad Stork; Daniel Svozil; Johannes Kirchmair (J. Chem. Inf. Model., 2017)

FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes. - Martin Sícho; Conrad Stork; Angelica Mazzolari; Christina de Bruyn Kops; Alessandro Pedretti; Bernard Testa; Giulio Vistoli; Daniel Svozil; Johannes Kirchmair (J. Chem. Inf. Model., 2019)

Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters. - Conrad Stork; Ya Chen 002; Martin Sícho; Johannes Kirchmair (J. Chem. Inf. Model., 2019)

NERDD: a web portal providing access to in silico tools for drug discovery. - Conrad Stork; Gerd Embruch; Martin Sícho; Christina de Bruyn Kops; Ya Chen 002; Daniel Svozil; Johannes Kirchmair (Bioinform., 2020)

Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop. - Fidele Ntie-Kang; Kiran K. Telukunta; Serge A. T. Fobofou; Victor Chukwudi Osamor; Samuel A. Egieyeh; Marilia Valli; Yannick Djoumbou Feunang; Maria Sorokina; Conrad Stork; Neann Mathai; Paul F. Zierep; Ana L. Chávez-Hernández; et al. (J. Cheminformatics, 2021)

MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn. - Jochen Sieg; Christian Feldmann; Jennifer Hemmerich; Conrad Stork; Frederik Sandfort; Philipp Eiden; Miriam Mathea (J. Chem. Inf. Model., 2024)